The MLG was originally developed in the mid 1980s. It has since been used for two decades as the underpinning for various particle dynamics simulations, including molecular dynamics and according to Cybyk, B., Oran, E. S., Boris, J. P., and Anderson, J. D., “Combining the Monotonic Lagrangian Grid with a direct simulation Monte Carlo Model,” J. of Computational Physics, 122, 323 (1995), it has been used in direct simulation Monte Carlo methods of simulating rarefied gas dynamics. The subject matter described herein represents a new application of the MLG, as a tool for air traffic modeling, and new capabilities have been added to the MLG for this purpose. Air traffic systems are vital to economic growth and stability; furthermore, safe and properly controlled air traffic systems are crucial to homeland security. In order to develop strategies for effective design and control of complex aspects of air transportation, a fast platform for modeling and predicting various layers of air traffic systems is needed.
A description of the multidimensional MLG is provided herein, involving the use of the multidimensional MLG in particle dynamics simulations. The MLG is a free-Lagrangian data structure for storing the positions and other data needed to describe N moving objects, where N can be very large. Here, the meaning of “object” or “node” depends on the particular application. That is, for molecular dynamics simulations, a node may correspond to an atom, while for direct simulation Monte Carlo applications, a node can describe a group of molecules. According to Sinkovits, R. S., Oran, E. S. and Boris, J. P., “A Technique for Regularizing the Structure of a Monotonic Lagrangian Grid,” J. of Computational Physics, 108, 368 (1993), and further according to Sinkovits, R. S., Boris, J. P. and Oran, E. S., “The Stability and Multiplicity of the Monotonic Lagrangian Grid,” NRL Memorandum Report 6410-97-7937, 1997, the MLG is not unique, and some MLGs are of higher quality than others.
In the extension of the multidimensional MLG technology disclosed here, a node or object corresponds to an aircraft and/or to other moving platforms. This work is applicable to both military and civil aviation, and to other systems where numerous entities are moving in complex paths relative to each other, such as swarms of mobile sensors and space debris.
The MLG is used as a tool for rapid prediction and modeling that enables active design of air transportation systems. There are other currently available air traffic simulation environments, such as NASA's Future Air Traffic Management Concepts Evaluation Tool (FACET) simulator. FACET is used to evaluate air traffic concepts, and is sometimes deployed in Federal Aviation Administration (FAA) operational tests. It uses the FAA's Enhanced Traffic Management System (ETMS) data along with wind and weather data from the National Oceanic and Atmospheric Administration (NOAA). These advanced capabilities can significantly slow-down the simulations.
Therefore, the need exists for advanced capabilities of rapid prediction and modeling algorithms to be added to the MLG for development of new modeling methodologies, relevant to air traffic systems. The new air traffic related algorithms include algorithms for collision avoidance between aircraft, for computing and updating aircraft trajectories, and for modifying trajectories to circumvent restricted zones (e.g., to avoid areas of bad weather or areas designated as government installations).
Further, the need exists for a very fast simulator that can be easily incorporated into the overall computational framework of modeling complex transport systems. The MLG model can quickly sort, track, and update positions of more than 10,000 aircraft, both on the ground (at airports) and in the air. It can be used to evaluate new system concepts; for instance, as an aid in testing various control strategies, such as collision avoidance, separation assurance, and traffic flow management, as well as in evaluating the reaction of the system to local and global perturbations, including atmospheric and/or weather events, such as lightning strikes, solar wind disruptions and/or volcanic eruptions. The MLG can be used to determine the most efficient way to reroute air traffic after local weather conditions, such as thunderstorms, have propagated a local disturbance throughout the entire system.